Optimization of Dispersed Energy Supply —Stochastic Programming with Recombining Scenario Trees
The steadily increasing share of wind energy within many power generating systems leads to strong and unpredictable fluctuations of the electricity supply and is thus a challenge with regard to power generation and transmission. We investigate the potential of energy storages to contribute to a cost optimal electricity supply by decoupling the supply and the demand. For this purpose we study a stochastic programming model of a regional power generating system consisting of thermal power units, wind energy, different energy storage systems, and the possibility for energy import. The identification of a cost optimal operation plan allows to evaluate the economical possibilities of the considered storage technologies.
On the one hand the optimization of energy storages requires the consideration of long-term planning horizons. On the other hand the highly fluctuating wind energy input requires a detailed temporal resolution. Consequently, the resulting optimization problem can, due to its dimension, not be tackled by standard solution approaches. We thus reduce the complexity by employing recombining scenario trees and apply a decomposition technique that exploits the special structure of those trees.
KeywordsWind Power Wind Energy Master Problem Scenario Tree Spot Market
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- 9.H. Heitsch and W. Römisch. Scenario tree modeling for multistage stochastic programs. Mathematical Programming to appear, 2008.Google Scholar
- 10.ILOG, Inc. CPLEX 10.0. http://www.ilog.com/products/cplex.
- 11.C. Küchler and S. Vigerske. Decomposition of multistage stochastic programs with recombining scenario trees. Stochastic Programming E-Print Series, 9, 2007. http://www.speps.org.
- 12.A. Ruszczyński. Stochastic Programming, A. Ruszczyński and A. Shapiro (Editors), chapter Decomposition Methods, chapter 3, pages 141–221. Elsevier, Amsterdam, 2003.Google Scholar
- 13.A. Ruszczyński and A. Shapiro, editors. Stochastic Programming. Handbooks in Operations Research and Management Science. Elsevier, Amsterdam, 2003.Google Scholar
- 15.D. Swider, P. Vogel, and C. Weber. Stochastic model for the european electricity market and the integration costs for wind power. Technical report, GreenNet Report on WP 6, 2004.Google Scholar
- 17.H.-J. Wagner. Wind Energy Utilization, N. Bansal and J. Mathur (Editors), chapter Wind Energy and Present Status in Germany. Anamaya, New Delhi, 2002.Google Scholar
- 18.C. Weber. Uncertainty in the Electric Power Industry: Methods and Models for Decision Support. Springer, New York, 2005.Google Scholar